{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             CRIM         ZN       INDUS         CHAS         NOX          RM  \\\n",
      "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
      "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
      "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
      "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
      "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
      "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
      "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
      "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
      "\n",
      "              AGE         DIS         RAD         TAX     PTRATIO       LSTAT  \\\n",
      "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
      "mean    68.574901    3.795043    9.549407  408.237154   18.455534   12.653063   \n",
      "std     28.148861    2.105710    8.707259  168.537116    2.164946    7.141062   \n",
      "min      2.900000    1.129600    1.000000  187.000000   12.600000    1.730000   \n",
      "25%     45.025000    2.100175    4.000000  279.000000   17.400000    6.950000   \n",
      "50%     77.500000    3.207450    5.000000  330.000000   19.050000   11.360000   \n",
      "75%     94.075000    5.188425   24.000000  666.000000   20.200000   16.955000   \n",
      "max    100.000000   12.126500   24.000000  711.000000   22.000000   37.970000   \n",
      "\n",
      "             MEDV  \n",
      "count  506.000000  \n",
      "mean    22.532806  \n",
      "std      9.197104  \n",
      "min      5.000000  \n",
      "25%     17.025000  \n",
      "50%     21.200000  \n",
      "75%     25.000000  \n",
      "max     50.000000  \n",
      "min_max归一化前x_data的最小值0.0和最大值711.0\n",
      "min_max归一化后x_data的最小值0.0和最大值1.0\n",
      "Train on 363 samples, validate on 41 samples\n",
      "Epoch 1/50\n",
      "363/363 [==============================] - 0s 91us/sample - loss: 555.8328 - val_loss: 528.5756\n",
      "Epoch 2/50\n",
      "363/363 [==============================] - 0s 24us/sample - loss: 472.0763 - val_loss: 452.6973\n",
      "Epoch 3/50\n",
      "363/363 [==============================] - 0s 39us/sample - loss: 405.2466 - val_loss: 392.2565\n",
      "Epoch 4/50\n",
      "363/363 [==============================] - 0s 36us/sample - loss: 351.6469 - val_loss: 345.0134\n",
      "Epoch 5/50\n",
      "363/363 [==============================] - 0s 30us/sample - loss: 309.4086 - val_loss: 306.3851\n",
      "Epoch 6/50\n",
      "363/363 [==============================] - 0s 34us/sample - loss: 274.6295 - val_loss: 275.8186\n",
      "Epoch 7/50\n",
      "363/363 [==============================] - 0s 25us/sample - loss: 246.8237 - val_loss: 251.3103\n",
      "Epoch 8/50\n",
      "363/363 [==============================] - 0s 28us/sample - loss: 224.3518 - val_loss: 230.4097\n",
      "Epoch 9/50\n",
      "363/363 [==============================] - 0s 34us/sample - loss: 205.0233 - val_loss: 214.9999\n",
      "Epoch 10/50\n",
      "363/363 [==============================] - 0s 32us/sample - loss: 190.6091 - val_loss: 201.9265\n",
      "Epoch 11/50\n",
      "363/363 [==============================] - 0s 31us/sample - loss: 178.2376 - val_loss: 191.1170\n",
      "Epoch 12/50\n",
      "363/363 [==============================] - 0s 24us/sample - loss: 167.9239 - val_loss: 182.5768\n",
      "Epoch 13/50\n",
      "363/363 [==============================] - 0s 24us/sample - loss: 159.7201 - val_loss: 175.2341\n",
      "Epoch 14/50\n",
      "363/363 [==============================] - 0s 33us/sample - loss: 152.5486 - val_loss: 169.5332\n",
      "Epoch 15/50\n",
      "363/363 [==============================] - 0s 37us/sample - loss: 146.9640 - val_loss: 164.2192\n",
      "Epoch 16/50\n",
      "363/363 [==============================] - 0s 28us/sample - loss: 141.7193 - val_loss: 159.4567\n",
      "Epoch 17/50\n",
      "363/363 [==============================] - 0s 23us/sample - loss: 136.9861 - val_loss: 155.7154\n",
      "Epoch 18/50\n",
      "363/363 [==============================] - 0s 36us/sample - loss: 133.2165 - val_loss: 152.7197\n",
      "Epoch 19/50\n",
      "363/363 [==============================] - 0s 26us/sample - loss: 130.3173 - val_loss: 149.7985\n",
      "Epoch 20/50\n",
      "363/363 [==============================] - 0s 35us/sample - loss: 127.3873 - val_loss: 147.1908\n",
      "Epoch 21/50\n",
      "363/363 [==============================] - 0s 22us/sample - loss: 124.7906 - val_loss: 144.6467\n",
      "Epoch 22/50\n",
      "363/363 [==============================] - 0s 29us/sample - loss: 122.2186 - val_loss: 142.5833\n",
      "Epoch 23/50\n",
      "363/363 [==============================] - 0s 28us/sample - loss: 120.2617 - val_loss: 140.5679\n",
      "Epoch 24/50\n",
      "363/363 [==============================] - 0s 22us/sample - loss: 118.3845 - val_loss: 138.7157\n",
      "Epoch 25/50\n",
      "363/363 [==============================] - 0s 23us/sample - loss: 116.6440 - val_loss: 136.9417\n",
      "Epoch 26/50\n",
      "363/363 [==============================] - 0s 31us/sample - loss: 114.9359 - val_loss: 135.2521\n",
      "Epoch 27/50\n",
      "363/363 [==============================] - 0s 24us/sample - loss: 113.3784 - val_loss: 133.5543\n",
      "Epoch 28/50\n",
      "363/363 [==============================] - 0s 26us/sample - loss: 111.7981 - val_loss: 131.9729\n",
      "Epoch 29/50\n",
      "363/363 [==============================] - 0s 22us/sample - loss: 110.3450 - val_loss: 130.5067\n",
      "Epoch 30/50\n",
      "363/363 [==============================] - 0s 24us/sample - loss: 109.0396 - val_loss: 129.0475\n",
      "Epoch 31/50\n",
      "363/363 [==============================] - 0s 26us/sample - loss: 107.7503 - val_loss: 127.6038\n",
      "Epoch 32/50\n",
      "363/363 [==============================] - 0s 29us/sample - loss: 106.4782 - val_loss: 126.1689\n",
      "Epoch 33/50\n",
      "363/363 [==============================] - 0s 33us/sample - loss: 105.2304 - val_loss: 124.7648\n",
      "Epoch 34/50\n",
      "363/363 [==============================] - 0s 29us/sample - loss: 103.9864 - val_loss: 123.4204\n",
      "Epoch 35/50\n",
      "363/363 [==============================] - 0s 27us/sample - loss: 102.8192 - val_loss: 122.0856\n",
      "Epoch 36/50\n",
      "363/363 [==============================] - 0s 19us/sample - loss: 101.6717 - val_loss: 120.8340\n",
      "Epoch 37/50\n",
      "363/363 [==============================] - 0s 21us/sample - loss: 100.5994 - val_loss: 119.4564\n",
      "Epoch 38/50\n",
      "363/363 [==============================] - 0s 28us/sample - loss: 99.4368 - val_loss: 118.2466\n",
      "Epoch 39/50\n",
      "363/363 [==============================] - 0s 62us/sample - loss: 98.4140 - val_loss: 117.0279\n",
      "Epoch 40/50\n",
      "363/363 [==============================] - 0s 53us/sample - loss: 97.3915 - val_loss: 115.7733\n",
      "Epoch 41/50\n",
      "363/363 [==============================] - 0s 48us/sample - loss: 96.3705 - val_loss: 114.6044\n",
      "Epoch 42/50\n",
      "363/363 [==============================] - 0s 22us/sample - loss: 95.4136 - val_loss: 113.4665\n",
      "Epoch 43/50\n",
      "363/363 [==============================] - 0s 46us/sample - loss: 94.4953 - val_loss: 112.2598\n",
      "Epoch 44/50\n",
      "363/363 [==============================] - 0s 27us/sample - loss: 93.5497 - val_loss: 111.1024\n",
      "Epoch 45/50\n",
      "363/363 [==============================] - 0s 34us/sample - loss: 92.6551 - val_loss: 109.9353\n",
      "Epoch 46/50\n",
      "363/363 [==============================] - 0s 38us/sample - loss: 91.7055 - val_loss: 108.7661\n",
      "Epoch 47/50\n",
      "363/363 [==============================] - 0s 30us/sample - loss: 90.8123 - val_loss: 107.7131\n",
      "Epoch 48/50\n",
      "363/363 [==============================] - 0s 20us/sample - loss: 89.9398 - val_loss: 106.6930\n",
      "Epoch 49/50\n",
      "363/363 [==============================] - 0s 31us/sample - loss: 89.0966 - val_loss: 105.6803\n",
      "Epoch 50/50\n",
      "363/363 [==============================] - 0s 26us/sample - loss: 88.3208 - val_loss: 104.6918\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集的均方误差为: 96.48895675060795\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测值: 21.621529\n",
      "标签值：32.400002\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# 读取数据\n",
    "df_pd = pd.read_csv(\"C:/Users/data/boston.csv\", header=0)\n",
    "print(df_pd.describe())\n",
    "\n",
    "# 将数据转为 NumPy 数组并进行洗牌\n",
    "df_np = np.array(df_pd, dtype=np.float32)\n",
    "df = shuffle(df_np)\n",
    "\n",
    "# 检查是否有 NaN 或 Inf 值\n",
    "if np.any(np.isnan(df)) or np.any(np.isinf(df)):\n",
    "    print(\"Data contains NaN or Inf values before normalization!\")\n",
    "\n",
    "# 特征数据和标签分离\n",
    "x_data = df[:, :12]\n",
    "y_data = df[:, 12].reshape(-1, 1)\n",
    "\n",
    "# 数据归一化\n",
    "print(f\"min_max归一化前x_data的最小值{np.min(x_data)}和最大值{np.max(x_data)}\")\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_data = min_max_scaler.fit_transform(x_data)\n",
    "print(f\"min_max归一化后x_data的最小值{np.min(x_data)}和最大值{np.max(x_data)}\")\n",
    "\n",
    "# 数据集拆分\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.2, random_state=42)\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.1, random_state=42)\n",
    "\n",
    "# 构建模型\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(1, input_dim=12, kernel_initializer='glorot_uniform')\n",
    "])\n",
    "\n",
    "# 编译模型\n",
    "model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.001),\n",
    "              loss='mean_squared_error')\n",
    "\n",
    "# 训练模型\n",
    "history = model.fit(x_train, y_train, epochs=50, batch_size=20, validation_data=(x_valid, y_valid))\n",
    "\n",
    "# 训练过程的损失曲线\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Validation Loss')\n",
    "plt.show()\n",
    "\n",
    "# 测试集评估\n",
    "test_loss = model.evaluate(x_test, y_test)\n",
    "print(f\"测试集的均方误差为: {test_loss}\")\n",
    "\n",
    "# 预测结果\n",
    "preds = model.predict(x_test)\n",
    "plt.figure()\n",
    "plt.xlabel(\"# sample\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.plot(preds, \"blue\", label=\"Predictions\")\n",
    "plt.plot(y_test, \"red\", label=\"Real values\")\n",
    "plt.legend(loc=1)\n",
    "plt.show()\n",
    "\n",
    "# 随机样本预测\n",
    "n = np.random.randint(len(x_test))\n",
    "x_new = x_test[n].reshape(1, 12)\n",
    "predict = model.predict(x_new)\n",
    "print(\"预测值: %f\" % predict)\n",
    "\n",
    "target = y_test[n]\n",
    "print(\"标签值：%f\" % target)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
